Local community extraction in directed networks

Network is a simple but powerful representation of real-world complex systems. Network community analysis has become an invaluable tool to explore and reveal the internal organization of nodes. However, only a few methods were directly designed for community-detection in directed networks. In this article, we introduce the concept of local community structure in directed networks and provide a generic criterion to describe a local community with two properties. We further propose a stochastic optimization algorithm to rapidly detect a local community, which allows for uncovering the directional modular characteristics in directed networks. Numerical results show that the proposed method can resolve detailed local communities with directional information and provide more structural characteristics of directed networks than previous methods.Comment: 8 pages, 6 figure

Local multiresolution order in community detection

Community detection algorithms attempt to find the best clusters of nodes in an arbitrary complex network. Multi-scale ("multiresolution") community detection extends the problem to identify the best network scale(s) for these clusters. The latter task is generally accomplished by analyzing community stability simultaneously for all clusters in the network. In the current work, we extend this general approach to define local multiresolution methods, which enable the extraction of well-defined local communities even if the global community structure is vaguely defined in an average sense. Toward this end, we propose measures analogous to variation of information and normalized mutual information that are used to quantitatively identify the best resolution(s) at the community level based on correlations between clusters in independently-solved systems. We demonstrate our method on two constructed networks as well as a real network and draw inferences about local community strength. Our approach is independent of the applied community detection algorithm save for the inherent requirement that the method be able to identify communities across different network scales, with appropriate changes to account for how different resolutions are evaluated or defined in a particular community detection method. It should, in principle, easily adapt to alternative community comparison measures.Comment: 19 pages, 11 figure

Community Catalyst: How Community Foundations Are Acting as Agents for Local Change

Presents the experiences, successes, failures, and lessons learned from the work of several community foundations. Uses case studies, interviews, and evaluation analysis to identify approaches for doing, as well as supporting, catalyst work

Community Development and Local Social Capital

While a substantial amount of research has been devoted to showing what social capital does, research explaining social capital itself lags behind. The literature has a long tradition of examining the effect of social capital on local economic growth and development. In this paper we examine whether local economic development can explain the variation in social capital across various geographical clusters in the state of Georgia. We begin by devising a measurement tool, a Human Development Index (HDI), to measure community development. Our social capital measure includes associational memberships, voluntary activities, and philanthropy obtained from the Georgia Social Capital Survey. The findings show that even after accounting for various demographic and economic characteristics, the HDI explains the variation in a number of social capital levels (especially those measured by associational involvement) across various geographical clusters in the state of Georgia.economic development, human development, social capital, Agribusiness, Community/Rural/Urban Development, Institutional and Behavioral Economics, Labor and Human Capital, Public Economics, R00,